The all-caps `clod` file, created via the `init` command, stores project structure and user-defined rules. Unlike temporary in-chat instructions that get lost or degraded as the conversation continues, this file is referenced in every session, ensuring consistent behavior and enforcing project-wide guardrails.

When an AI agent like Claude Code nears its context limit where automatic compaction might fail, a useful hack is instructing it to "write a markdown file of your process and your progress and what you have left to do." This creates a manual state transfer mechanism for starting a new session.

Instead of manually providing context in each prompt, use Claude Code's 'append system prompt' command. This preloads crucial information, like architectural diagrams, at the start of a session, leading to faster and more accurate AI responses without repeated file reads.

Don't try to create a comprehensive "memory" for your AI in one sitting. Instead, adopt a simple rule: whenever you find yourself explaining context to the AI, stop and immediately have it capture that information in a permanent context file. This makes personalization far more manageable.

The easiest way to teach Claude Code is to instruct it: "Don't make this mistake again; add this to `claude.md`." Since this file is always included in the prompt context, it acts as a permanent, evolving set of instructions and guardrails for the AI.

Long, continuous AI chat threads degrade output quality as the context window fills up, making it harder for the model to recall early details. To maintain high-quality results, treat each discrete feature or task as a new chat, ensuring the agent has a clean, focused context for each job.

Most users re-explain their role and situation in every new AI conversation. A more advanced approach is to build a dedicated professional context document and a system for capturing prompts and notes. This turns AI from a stateless tool into a stateful partner that understands your specific needs.

Instead of overloading the context window, encapsulate deep domain knowledge into "skill" files. Claude Code can then intelligently pull in this information "just-in-time" when it needs to perform a specific task, like following a complex architectural pattern.

Notion's team uses a `claude.md` file in their repo root to provide global instructions (e.g., tech stack) to their AI assistant. A git-ignored `claude.local.md` file is then used by each developer to provide personal context, like their username, which prevents the AI from modifying others' work.